Compiler-Based Approach to Enhance BliMe Hardware Usability
Bibliographic record
Abstract
Outsourced computing has emerged as an efficient platform for data processing, but it has raised security concerns due to potential exposure of sensitive data through runtime and side-channel attacks. To address these concerns, the BliMe hardware extensions offer a hardware-enforced taint tracking policy to prevent secret-dependent data exposure. However, such strict policies can hinder software usability on BliMe hardware. \n \nWhile existing solutions can transform software to make it constant-time and more compatible with BliMe policies, they are not fully compatible with BliMe hardware. To strengthen the usability of BliMe hardware, we propose a compiler-based tool to detect and transform policy violations, ensuring constant-time compliance with BliMe. Our tool employs static analysis for taint tracking and employs transformation techniques including array access expansion, control-flow linearization and branchless select. We have implemented the tool on LLVM-11 to automatically convert existing source code. \n \nWe then conducted experiments on WolfSSL and OISA to examine the accuracy of the analysis and the effect of the transformations. Our evaluation indicates that our tool can successfully transform multiple code patterns. However, we acknowledge that certain code patterns are challenging to transform. Therefore, we also discuss manual approaches and explore potential future work to expand the coverage of our automatic transformations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".